Yu, Dong

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 10,260

Deep Learning: Methods and Applications

Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 3,480

Recent advances in deep learning for speech research at Microsoft

Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 801

The microsoft 2016 conversational speech recognition system

Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., & Zweig, G. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 326

Toward Human Parity in Conversational Speech Recognition

Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M. L., Stolcke, A., Yu, D., & Zweig, G. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 245

Replay and Synthetic Speech Detection with Res2Net Architecture

Li, X., Li, N., Weng, C., Liu, X., Su, D., Yu, D., & Meng, H. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisBiometric Identification and Security

Cited by 186

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

Lei, J., Wang, L., Shen, Y., Yu, D., Berg, T., & Bansal, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multimodal Machine Learning ApplicationsVideo Analysis and Summarization

Cited by 179

FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

Huang, R., Lam, M. W. Y., Wang, J., Su, D., Yu, D., Ren, Y., & Zhao, Z. (2022) · Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 96

InFoBench: Evaluating Instruction Following Ability in Large Language Models

Qin, Y., Song, K., Hu, Y., Yao, W., Cho, S., Wang, X., Wu, X., Liu, F., Liu, P., & Yu, D. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingNatural Language Processing Techniques

Cited by 77

Dense X Retrieval: What Retrieval Granularity Should We Use?

Chen, T., Wang, H., Chen, S., Yu, W., Ma, K., Zhao, X., Zhang, H., & Yu, D. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 74

Dense X Retrieval: What Retrieval Granularity Should We Use?

Chen, T., Wang, H., Chen, S., Yu, W., Ma, K., Zhao, X., Zhang, H., & Yu, D. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 74

Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension

Gong, H., Shen, Y., Yu, D., Chen, J., & Yu, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 42

A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning

Hong, R., Zhang, H., Pang, X., Yu, D., & Zhang, C. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 11